Iterative Filtering Based on Adaptive Chebyshev Kernel Functions for Noise Supression in Differential SAR Interferograms

Abstract

Differential SAR Interferometry (DInSAR) is a powerful remote sensing technique employed to monitor surface displacements, such as ground subsidence or strong deformations caused by geological activity. The quality of the interferometric phase between two combined SAR images is essential for the estimation of the surface deformation. Multi-pIe decorrelation factors may degrade the quality of the measurements and, then, the development of filtering methods for noise suppression is mandatory. In this work, we propose a new strategy to improve noise reduction while preserving the original phase structure. The new method consists in an iterative filter in which noise reduction is achieved progressively. The original phase is filtered with adaptive kernels based on Chebyshev interpolation functions. The filter is especially useful for DInSAR geophysical applications, such as earthquakes or volcanic eruptions monitoring. The performance of the proposed method has been tested with both simulated data and recently acquired Sentinel-1 SAR data which mapped the August 2016 Central Italy earthquake.